Bottom Line:
The model is significantly improved by adding terrain roughness.The effect amounts to an 18% reduction in the number of tornadoes for every ten meter increase in elevation standard deviation.Flexibility of the model is illustrated by fitting it to data from Illinois, Mississippi, South Dakota, and Ohio.

Affiliation: Department of Geography, Florida State University, Tallahassee, Florida, United States of America.

ABSTRACTTornado reports are locally rare, often clustered, and of variable quality making it difficult to use them directly to describe regional tornado climatology. Here a statistical model is demonstrated that overcomes some of these difficulties and produces a smoothed regional-scale climatology of tornado occurrences. The model is applied to data aggregated at the level of counties. These data include annual population, annual tornado counts and an index of terrain roughness. The model has a term to capture the smoothed frequency relative to the state average. The model is used to examine whether terrain roughness is related to tornado frequency and whether there are differences in tornado activity by County Warning Area (CWA). A key finding is that tornado reports increase by 13% for a two-fold increase in population across Kansas after accounting for improvements in rating procedures. Independent of this relationship, tornadoes have been increasing at an annual rate of 1.9%. Another finding is the pattern of correlated residuals showing more Kansas tornadoes in a corridor of counties running roughly north to south across the west central part of the state consistent with the dryline climatology. The model is significantly improved by adding terrain roughness. The effect amounts to an 18% reduction in the number of tornadoes for every ten meter increase in elevation standard deviation. The model indicates that tornadoes are 51% more likely to occur in counties served by the CWAs of DDC and GID than elsewhere in the state. Flexibility of the model is illustrated by fitting it to data from Illinois, Mississippi, South Dakota, and Ohio.

pone.0131876.g001: Kansas counties and elevation.Counties are labeled by the corresponding CWA. Elevation is given at a resolution of 80 m.

Mentions:
The model is written with the open-source R language using freely-available government data including tornadoes from the U.S. Storm Prediction Center (SPC), population and administrative boundaries from the U.S. Census Bureau, and elevations from NASA’s Shuttle Radar Topography Mission (SRTM). The data are prepared as follows. First county administrative boundaries for the United States are downloaded and read into R as vector polygons from https://www.census.gov/geo/maps-data/data/cbf/cbf_counties.html at a resolution of 1:5 million and subset by the state of interest using the Federal Information Processing Standard (FIPS) code. Then digital elevation model (DEM) data are downloaded from http://www.viewfinderpanoramas.org at a resolution of three arc seconds (approximately 80 m) and read into R as a raster. The elevation raster is cropped to the state boundary. Next CWA labels from http://www.nws.noaa.gov/geodata/catalog/wsom/data/bp03de14.dbx are attached to each county. The results for Kansas are displayed on a map in Fig 1.

pone.0131876.g001: Kansas counties and elevation.Counties are labeled by the corresponding CWA. Elevation is given at a resolution of 80 m.

Mentions:
The model is written with the open-source R language using freely-available government data including tornadoes from the U.S. Storm Prediction Center (SPC), population and administrative boundaries from the U.S. Census Bureau, and elevations from NASA’s Shuttle Radar Topography Mission (SRTM). The data are prepared as follows. First county administrative boundaries for the United States are downloaded and read into R as vector polygons from https://www.census.gov/geo/maps-data/data/cbf/cbf_counties.html at a resolution of 1:5 million and subset by the state of interest using the Federal Information Processing Standard (FIPS) code. Then digital elevation model (DEM) data are downloaded from http://www.viewfinderpanoramas.org at a resolution of three arc seconds (approximately 80 m) and read into R as a raster. The elevation raster is cropped to the state boundary. Next CWA labels from http://www.nws.noaa.gov/geodata/catalog/wsom/data/bp03de14.dbx are attached to each county. The results for Kansas are displayed on a map in Fig 1.

Bottom Line:
The model is significantly improved by adding terrain roughness.The effect amounts to an 18% reduction in the number of tornadoes for every ten meter increase in elevation standard deviation.Flexibility of the model is illustrated by fitting it to data from Illinois, Mississippi, South Dakota, and Ohio.

Affiliation:
Department of Geography, Florida State University, Tallahassee, Florida, United States of America.

ABSTRACTTornado reports are locally rare, often clustered, and of variable quality making it difficult to use them directly to describe regional tornado climatology. Here a statistical model is demonstrated that overcomes some of these difficulties and produces a smoothed regional-scale climatology of tornado occurrences. The model is applied to data aggregated at the level of counties. These data include annual population, annual tornado counts and an index of terrain roughness. The model has a term to capture the smoothed frequency relative to the state average. The model is used to examine whether terrain roughness is related to tornado frequency and whether there are differences in tornado activity by County Warning Area (CWA). A key finding is that tornado reports increase by 13% for a two-fold increase in population across Kansas after accounting for improvements in rating procedures. Independent of this relationship, tornadoes have been increasing at an annual rate of 1.9%. Another finding is the pattern of correlated residuals showing more Kansas tornadoes in a corridor of counties running roughly north to south across the west central part of the state consistent with the dryline climatology. The model is significantly improved by adding terrain roughness. The effect amounts to an 18% reduction in the number of tornadoes for every ten meter increase in elevation standard deviation. The model indicates that tornadoes are 51% more likely to occur in counties served by the CWAs of DDC and GID than elsewhere in the state. Flexibility of the model is illustrated by fitting it to data from Illinois, Mississippi, South Dakota, and Ohio.